Pemetaan Risiko Pembiayaan Konsumen Berbasis Clustering Wilayah Pengajuan

Muchammad Ubaidillah; Yitno Utomo

doi:10.69693/ijim.v4i3.1888

Authors

Muchammad Ubaidillah Universitas PGRI Adi Buana Surabaya
Yitno Utomo Universitas PGRI Adi Buana Surabaya

DOI:

https://doi.org/10.69693/ijim.v4i3.1888

Keywords:

K-Means, Risiko Pembiayaan, Segmentasi Wilayah, Keterlambatan Pembayaran, Data Mining

Abstract

Risiko gagal bayar pada perusahaan pembiayaan tidak selalu tersebar secara merata karena karakteristik pembayaran konsumen dapat berbeda antarwilayah. Penelitian ini bertujuan memetakan tingkat risiko pembiayaan berdasarkan wilayah pengajuan konsumen serta merumuskan strategi pengendalian yang sesuai dengan profil setiap kelompok. Penelitian menggunakan pendekatan kuantitatif deskriptif dengan studi kasus pada perusahaan pembiayaan di Sidoarjo. Data sekunder mencakup 712 konsumen yang tersebar pada 40 wilayah selama 2025 dan dikelompokkan berdasarkan proporsi pembayaran tepat waktu, keterlambatan 1–90 hari, serta keterlambatan 91–210 hari. Data dibersihkan, ditransformasi menjadi proporsi, kemudian dianalisis menggunakan metode Elbow, algoritma K-Means berbasis jarak Euclidean, dan evaluasi deskriptif antarcluster. Hasil menunjukkan bahwa tiga cluster merupakan struktur yang paling informatif. Cluster 1 terdiri atas 13 wilayah dengan profil WARNING, cluster 2 mencakup 19 wilayah dengan profil EXCELLENT, dan cluster 3 berisi 8 wilayah dengan profil BAD. Centroid cluster berisiko tinggi memperlihatkan proporsi pembayaran tepat waktu 0,6901, keterlambatan 1–90 hari 0,2661, dan keterlambatan 91–210 hari 0,0438. Wilayah Jabon menunjukkan proporsi keterlambatan panjang tertinggi. Penelitian menyimpulkan bahwa clustering wilayah dapat menyediakan dasar objektif untuk diferensiasi kebijakan pembiayaan. Implikasinya, perusahaan perlu menerapkan pemantauan preventif pada wilayah WARNING, ekspansi terukur pada wilayah EXCELLENT, serta pengetatan persetujuan, kunjungan lapangan, dan restrukturisasi pada wilayah BAD.

Downloads

Download data is not yet available.

References

Ahsina, N. H., Fatimah, F., & Rachmawati, F. 2022. Customer Segmentation Analysis Based on Credit Using K-Means Clustering. Jurnal Ilmiah Teknologi Informasi Terapan, 8(3). Bandung: Universitas Widyatama. https://doi.org/10.33197/jitter.vol8.iss3.2022.883.

Akbar, M. N., Salsabila, A., Asri, A. P., & Syawir, M. 2023. Clustering Analysis for Credit Card User Segmentation Using K-Means Algorithm and Principal Component Analysis. AGENTS: Journal of Artificial Intelligence and Data Science, 3(1). Makassar: UIN Alauddin. https://doi.org/10.24252/jagti.v3i1.56.

Alonso-Robisco, A., & Carbó, J. M. 2022. Measuring the Model Risk-Adjusted Performance of Machine Learning Algorithms in Credit Default Prediction. Financial Innovation, 8, 70. Berlin: Springer Nature. https://doi.org/10.1186/s40854-022-00366-1.

Barragán Garnica, D. 2023. Patrones de Comportamiento de Clientes con Tarjetas de Crédito con Deterioro de Calificación por Riesgo Utilizando K-Means. ODEON, 22, 7–37. Bogotá: Universidad Externado de Colombia. https://doi.org/10.18601/17941113.n22.02.

Baser, F., Koc, O., & Selcuk-Kestel, A. S. 2023. Credit Risk Evaluation Using Clustering Based Fuzzy Classification Method. Expert Systems with Applications, 223, 119882. Amsterdam: Elsevier. https://doi.org/10.1016/j.eswa.2023.119882.

Bazzana, F., Bee, M., & Khatir, A. A. H. A. 2024. Machine Learning Techniques for Default Prediction: An Application to Small Italian Companies. Risk Management, 26, 1. London: Palgrave Macmillan. https://doi.org/10.1057/s41283-023-00132-2.

Bosker, J., Gürtler, M., & Zöllner, M. 2025. Machine Learning-Based Variable Selection for Clustered Credit Risk Modeling. Journal of Business Economics, 95(4), 617–652. Berlin: Springer. https://doi.org/10.1007/s11573-024-01213-8.

Carleo, A., & Rocci, R. 2024. Functional Clustering of NPLs Recovery Curves. Socio-Economic Planning Sciences, 95, 102018. Amsterdam: Elsevier. https://doi.org/10.1016/j.seps.2024.102018.

Caruso, G., Gattone, S. A., Fortuna, F., & Di Battista, T. 2021. Cluster Analysis for Mixed Data: An Application to Credit Risk Evaluation. Socio-Economic Planning Sciences, 73, 100850. Amsterdam: Elsevier. https://doi.org/10.1016/j.seps.2020.100850.

Dang, C., Chen, X., Yu, S., Chen, R., & Yang, Y. 2022. Credit Ratings of Chinese Households Using Factor Scores and K-Means Clustering Method. International Review of Economics & Finance, 78, 309–320. Amsterdam: Elsevier. https://doi.org/10.1016/j.iref.2021.12.014.

Gavira-Durón, N., Gutierrez-Vargas, O., & Cruz-Aké, S. 2021. Markov Chain K-Means Cluster Models and Their Use for Companies’ Credit Quality and Default Probability Estimation. Mathematics, 9(8), 879. Basel: MDPI. https://doi.org/10.3390/math9080879.

Jamotton, C., Hainaut, D., & Hames, T. 2024. Insurance Analytics with Clustering Techniques. Risks, 12(9), 141. Basel: MDPI. https://doi.org/10.3390/risks12090141.

Machado, M. R., & Karray, S. 2022a. Applying Hybrid Machine Learning Algorithms to Assess Customer Risk-Adjusted Revenue in the Financial Industry. Electronic Commerce Research and Applications, 56, 101202. Amsterdam: Elsevier. https://doi.org/10.1016/j.elerap.2022.101202.

Machado, M. R., & Karray, S. 2022b. Assessing Credit Risk of Commercial Customers Using Hybrid Machine Learning Algorithms. Expert Systems with Applications, 200, 116889. Amsterdam: Elsevier. https://doi.org/10.1016/j.eswa.2022.116889.

Ocktavia, S., & Atmojo, W. T. 2025. Analisis Segmentasi Pelanggan Pembiayaan Berdasarkan Demografi untuk Memprediksi Tingkat Kredit Menggunakan Algoritma K-Means. Jurnal Informatika Teknologi dan Sains, 7(1). Sumbawa: Universitas Teknologi Sumbawa. https://doi.org/10.51401/jinteks.v7i1.5582.

Rizkyanto, H., & Gaol, F. L. 2023. Customer Segmentation of Personal Credit Using Recency, Frequency, Monetary and K-Means on Financial Industry. International Journal of Advanced Computer Science and Applications, 14(4). London: The Science and Information Organization. https://doi.org/10.14569/IJACSA.2023.0140417.

Shi, S., Tse, R., Luo, W., D’Addona, S., & Pau, G. 2022. Machine Learning-Driven Credit Risk: A Systematic Review. Neural Computing and Applications, 34, 14327–14339. London: Springer. https://doi.org/10.1007/s00521-022-07472-2.

Wewengkang, F. S., & Wibowo, A. 2026. Analysis of Online Loan Regional Clustering in Indonesia in 2024 Based on Outstanding and Default Rate Using K-Means Clustering. Al-Kharaj: Journal of Islamic Economic and Business, 8(1). Palopo: IAIN Palopo. https://doi.org/10.24256/kharaj.v8i1.9342.

Yu, T., Huang, W., Tang, X., & Zheng, D. 2025. A Hybrid Unsupervised Machine Learning Model with Spectral Clustering and Semi-Supervised Support Vector Machine for Credit Risk Assessment. PLoS ONE, 20(1), e0316557. San Francisco: Public Library of Science. https://doi.org/10.1371/journal.pone.0316557.

Yuan, K., Chi, G., Zhou, Y., & Yin, H. 2022. A Novel Two-Stage Hybrid Default Prediction Model with K-Means Clustering and Support Vector Domain Description. Research in International Business and Finance, 59, 101536. Amsterdam: Elsevier. https://doi.org/10.1016/j.ribaf.2021.101536.